Mitochondria, Inflammation, and T-Cell Metabolism in a Rat Model of Pediatric Mild Traumatic Brain Injury
Bibliographic record
Abstract
Representing approximately 20,000 emergency department visits in Canada every year, pediatric traumatic brain injury (TBI) can be an intractable medical problem with limited treatment options. While most research has been directed towards the devastating, moderate-severe end of the TBI spectrum, most clinical injuries present as mild with minimal duration of loss of consciousness and lack of macroscopic damage to neural tissue. The pediatric population is especially vulnerable to the consequences of these milder injuries as developmental processes and long-term functioning can be impacted by negative cognitive and emotional changes persisting for up to and beyond one month after injury. Although we have some understanding of TBI pathophysiology including diffuse axonal injury, mitochondrial dysfunction, and cerebral blood flow dysregulation, there is still no clear understanding as to how the developing brains responds and adapts to injury. This thesis takes up the challenge of studying a mild, heterogeneous injury using a juvenile rat TBI model. It begins to unravel some of the complex pathophysiological patterns after pediatric mTBI from the perspectives of mitochondrial function, inflammation, and T-cell metabolism. First, we documented females having greater mitochondrial oxygen consumption in brain cells 21 days after a single mTBI, offering insight into one mechanism for persistent impairments in females following pediatric mTBI. Second, we highlight inflammatory changes to the understudied cerebellum, show cytokines as poor biomarkers of mTBI, and illustrate dynamic changes in inflammation after pediatric mTBI using network analysis. Third, we found preliminary evidence of metabolic changes in CD4+ T-cells starting at 24h post-mTBI, revealing possible upstream changes to observed inflammation previously shown only at 4-7 days after injury. Seeing changes in substrate oxidation patterns presaging inflammation may reveal nascent benefits to targeting metabolism to alter inflammation for therapeutic intervention. Collectively, the work in this thesis significantly advances our knowledge of pediatric mTBI pathophysiology, introduces new ways to interpret inflammation data, and paves the way for the investigation of novel pathways for therapeutic intervention.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".